Stellitron
Pre-Deployment Stability Audits
Pre-Deployment Stability Audits
The Problem: Unpredictable LLM Failure Modes
Enterprise LLM deployment is gated by unpredictable failure modes. Standard QA/testing fails to detect 'synthetic instabilities' or systemic failure vectors that manifest only under high stress, leading to catastrophic reputational damage, regulatory non-compliance, and service outages.
- ⚠Latent Instability: Synthetic failure modes only emerge under high-entropy, production stress (not caught in dev).
- ⚠Reputational Damage: Unsecured LLMs handling customer interaction can lead to public failures and brand erosion.
- ⚠Regulatory Risk: Lack of auditable stability metrics prevents adoption in high-stakes, regulated industries.
Stellitron: The Mandatory Stability Gate
We provide a mandatory pre-deployment stability gate using proprietary adversarial testing methodologies (inspired by PsAIch) that stress-test LLMs across thousands of synthetic, high-entropy scenarios. This generates an objective 'Stability Score' and a detailed failure threshold map, ensuring models are operationally resilient before entering production.
1. Adversarial Injection
Proprietary PsAIch-like methodology injects high-entropy, synthetic stress vectors into the model.
2. Latent Failure Mapping
Identify and map specific failure thresholds and modes that standard testing misses.
3. Stability Certification
Generate an objective, regulatory-ready 'Stability Score' required for production deployment.
System Architecture
- LLM/Model Artifacts (Hugging Face, Azure, AWS)
- Enterprise Policy/Compliance Rules
- Synthetic Adversarial Prompts
- Stellitron PsAIch Stress Engine (Proprietary IP)
- Failure Mode Classifier & Mitigation Suggestions
- Regulatory Reporting Module
- Stellitron Stability Score (0-100)
- Failure Threshold Map & Mitigation Report
- Audit Log for Compliance
- MLOps Pipelines (MLflow, Weights & Biases)
- CI/CD Tools (GitLab, Jenkins)
- Governance Dashboards (GRC Tools)
Why This Is Hard to Copy
- ✓Proprietary PsAIch-like Adversarial Methodology: Complex IP derived from advanced control theory and adversarial AI frameworks.
- ✓Regulatory Certification Standard: Deep integration with evolving global AI governance frameworks (EU AI Act, NIST).
- Failure Mode Data Flywheel: Continuous collection of unique enterprise failure mode data strengthens predictive stability models.
- Vendor-Agnostic Stress Testing: Ability to audit diverse proprietary and open-source LLM architectures efficiently.
- Category Creation: We define and own the 'Pre-Deployment Stability Audit' mandatory gate, unlike general security platforms.
- Focus on Operational Resilience: Dedicated to predicting synthetic instability, not just prompt injection/data security.
- Improves with each new regulation supported (compliance moat strengthens over time).
- Customer switching costs increase after integration into critical MLOps pipelines.
- Dataset compounding advantage in enterprise failure modes, making our Stability Score the most accurate predictor.
Market Opportunity: The $12B Assurance Gap
“The LLM security and stability auditing segment is a hyper-growth niche, driven by mandatory governance requirements and the urgency to secure GenAI systems.”
Competitive Landscape: Specialized Differentiation
Competitive Landscape
| Feature | Anthropic | SentinelOne | Cycode | Stellitron (Us) |
|---|---|---|---|---|
| Deep Synthetic Stability Audits (Our Focus) | Low | Low | Low | High |
| General Prompt Injection/Data Security | Medium | High | Medium | Medium |
| Pre-Deployment Mandatory Gate Integration | Low | Medium | Medium | High |
| Vendor-Agnostic LLM Support | Low (Model Specific) | Medium | Medium | High |
Business Model: High-Value, Recurring Revenue
1. Enterprise Platform Subscription
Annual recurring subscription for continuous access to the Stellitron platform, API, and compliance reporting module. Priced based on number of models and users.
2. Usage-Based Audit Fees
Variable fees charged per deep stability audit or stress-test run, based on computational intensity (GPU hours) and report complexity. Aligns cost with DevSecOps velocity.
3. Regulatory Certification Premium
Annual premium for models requiring official Stellitron Stability Certification for specific regulated deployments (e.g., Finance, Healthcare), including dedicated audit review.
Traction & Validation (Q1 2026 Status)
““Stellitron’s Stability Score has become the critical pre-deployment gate we trust, giving our risk committee confidence that our LLMs won't fail under stress.” – VP of AI Risk, Major Telecom Client.”
Product Roadmap & GTM
SOC 2 Type 1 & MLOps Integration
Q1 2026Achieve SOC 2 Type 1 compliance and establish integration partnerships with major MLOps platforms (e.g., Weights & Biases, MLflow).
Stability Certification MVP 2.0 Launch
Q2 2026Launch MVP 2.0 with automated reporting tailored for EU AI Act and NIST regulatory submission.
Full Enterprise Automation
Q4 2026Implement fully autonomous, scheduled auditing and mitigation suggestion engine.
Market Entry Strategy
- ▹Targeted Direct Sales to CISOs/CROs in Regulated Industries (Finance, Healthcare).
- ▹Partnerships with MLOps/DevSecOps platforms for mandatory pipeline integration.
- ▹Thought Leadership defining the 'Stability Score' as the industry standard.
Key Objectives
- ▹Secure 5 large enterprise contracts by EOY 2026.
- ▹Achieve $2M ARR by EOY 2026.
- ▹Establish Stellitron Stability Score as the compliance benchmark.
Financial Projections
Yearly Revenue Projections
Operating Assumptions & Burn Logic
Key Performance Indicators
The Ask: $3 Million Seed Round
Exit Strategy: Acquisition by Platform or Cyber Giants
Exit Scenarios
Comparable Exits
Risk Analysis & Mitigation
Risk Analysis & Mitigation
Major LLM platform providers bundle basic, free pre-deployment audit tools, commoditizing the core service.
Focus on vendor-agnostic auditing, specializing in deep, adversarial robustness testing and highly specific compliance frameworks (e.g., EU AI Act readiness) that internal tools lack the incentive to provide.
Audits are computationally intensive, leading to high operational costs (COGS) and slow turnaround times.
Optimize audit algorithms for efficiency and leverage highly parallelized cloud computing resources. Offer tiered service models (quick scan vs. deep audit) to manage resource consumption and pricing.
High R&D costs for specialized AI safety researchers and long enterprise sales cycles (9-18 months) for a new governance category.
Secure sufficient runway (24+ months) in initial funding. Prioritize initial GTM efforts on highly regulated sectors (Finance, Healthcare) with existing compliance budgets and clear mandates for AI risk management.
Rapidly evolving global regulations (EU AI Act, NIST) necessitate constant, expensive product redesigns.
Build a modular 'compliance engine' that allows adaptation to new regulatory standards via configuration (rule-sets) rather than core code changes. Hire dedicated regulatory counsel.
Sources & References
Generated by
Stellitron AI
Data Sources
Stellitron Internal Financial Model 2026-2030
Exa AI Web Search Data (February 2026)
Industry Reports (Forrester, Deloitte)
Public Financial Data
References
Global Tech Market Forecast, 2024 To 2029
Market Analysis (TAM/Growth)
Forrester: Global Tech Spend To Surpass $4.9 Trillion In 2025
Market Trend Validation
Anthropic AI Safety Research (Constitutional AI)
Problem Validation (Failure Rates)
Deloitte 2024 Risk Outlook
Problem Validation (Cost Impact)
Crunchbase & Company Websites
Competitive Intelligence
For inquiries, contact:
contact@stellitron.comThis pitch deck is for illustrative purposes. All financial projections, valuations, and market data are estimates and should be validated with professional advisors.